Triple
T3516066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Expedition of the Thousand |
E74309
|
entity |
| Predicate | arrivalPoint |
P1522
|
FINISHED |
| Object | Marsala |
E54390
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Marsala | Statement: [Expedition of the Thousand, arrivalPoint, Marsala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marsala Context triple: [Expedition of the Thousand, arrivalPoint, Marsala]
-
A.
Marsala
chosen
Marsala is a coastal city in western Sicily, Italy, best known for producing the fortified wine that shares its name.
-
B.
Musso
Musso is a small town on the western shore of Lake Como in northern Italy, known for its scenic lakeside setting and historic connections.
-
C.
Malvasía wine
Malvasía wine is a distinctive, often sweet white wine made from Malvasia grapes, traditionally produced in various Mediterranean and Atlantic regions.
-
D.
Chianti
Chianti is a renowned Italian red wine region in Tuscany, famous for its Sangiovese-based wines and picturesque rolling vineyards.
-
E.
Calimete
Calimete is a rural municipality in western Cuba known for its agricultural activities within Matanzas Province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc30362c81908ca7497a6a935cc6 |
completed | March 8, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e7da9c08190ab417b45339513bd |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:19 p.m.